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A Machine Learning Approach to Sentiment Analysis in Multilingual Web Texts (2009)
| Venue: | Information Retrieval |
| Citations: | 13 - 1 self |
BibTeX
@ARTICLE{Boiy09amachine,
author = {Erik Boiy and Marie-francine Moens},
title = {A Machine Learning Approach to Sentiment Analysis in Multilingual Web Texts},
journal = {Information Retrieval},
year = {2009},
pages = {526--558}
}
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Abstract
Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classification models that can be configured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83 % accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 % and 68 % respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated. Keywords Opinion mining – information tracking – cross-language learning – active learning 1
Keyphrases
sentiment analysis multilingual web text machine learning approach noisy character several classification model particular entity input text standard language active learning technique several problem unigram feature active learning certain consumption product commercial interest cascaded pipeline accuracy result forum text training pattern neutral feeling linguistic feature english text information extraction keywords opinion world wide web training set linguistic expression substantial part certain entity learned model french text opinion mining example sentence small size
